ppEffect Report
Overview of the dataset
| Attribute | Content |
|---|---|
| Object name | ara_root_simple |
| Cell number | 4709 |
| Gene number | 3909 |
| Active assay | SCT |
| Reductions | pca, umap, tsne |
| Up-regulated pp.Score (Q2) | 0.091792 |
| Up-regulated Groups in terms of Q2 | 0 , 1 , 3 , 4 , 10 , 11 |
| Up-regulated pp.Score (Q3) | 0.2274613 |
| Up-regulated Groups in terms of Q3 | 1 , 3 , 4 |
| Down-regulated pp.Score (Q2) | -0.072741 |
| Down-regulated Groups in terms of Q2 | 1 , 3 , 4 , 7 , 10 , 11 , 15 |
| Down-regulated pp.Score (Q1) | -0.012831 |
| Down-regulated Groups in terms of Q1 | 7 , 10 , 15 |
Gene number: representing the gene numbers in the activate assay.
Reductions: representing the dimension reduction methods conducted in this dataset.
Up-regulated pp.Score (Q2) : representing the median expression value of enzymolysis induced genes (Up_ppDEGs).
Up-regulated Groups in terms of Q2 : calculating the median of pp.Score in each cell group, then to figure out which clusters have higher pp.Scores than the pp.Score (Q2).
Up-regulated pp.Score (Q3) : representing The third quartile(Q3), also known as the higher quartile, is equal to the 75% of all the values in the sample arranged from the smallest to the largest of enzymolysis induced genes (Up_ppDEGs).
Up-regulated Groups in terms of Q3 : calculating the median of pp.Score in each cell group, then to figure out which clusters have higher pp.Scores than the pp.Score (Q3).
Down-regulated pp.Score (Q2) : representing the median expression value of enzymolysis induced genes (Down_ppDEGs).
Down-regulated Groups in terms of Q2 : calculating the median of pp.Score in each cell group, then to figure out which clusters have lower pp.Scores than the pp.Score (Q2).
Down-regulated pp.Score (Q1) : representing The first quartile(Q1), also known as the lower quartile, is equal to the smallest 25% of all the pp.Score values of enzymolysis induced genes (Down_ppDEGs).
Down-regulated Groups in terms of Q1 : calculating the median of pp.Score in each cell group, then to figure out which clusters have lower pp.Scores than the pp.Score (Q1).
Overlap between ppDEGs and HVGs
In this dataset, we totally have 3000 highly variable genes (HVGs), and all the HVGs will be used for PCA analysis following up.
The number of Up_ppDEGs is 917. The intersection between HVGs and Up_ppDEGs were 344 (genes), which means that 37.51% Up_ppDEGs belong to HVGs.
The number of Down_ppDEGs is 2518.The intersection between HVGs and Down_ppDEGs were 688 (genes), which means that 27.32% Down_ppDEGs belong to HVGs.
The extent of ppEffects in each cell
In this panel, your can see the distribution of ppEffects in each cells by UMAP plot. The darker the cell color, the more severely it is affected by enzymatic hydrolysis
